69062 - Statistical Methods for Financial Markets

Course Unit Page

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education

Academic Year 2019/2020

Learning outcomes

The course deals with the introduction to financial risk analysis and portfolio management. In its first part, the course covers the basics of time series analysis with particular emphasis on ARIMA modelling and forecasting. The second part deals with the analysis of financial time series.

Course contents

Modulo 1 - Introduction to time series analysis

  1. Basic definitions and main motivations.
  2. Introduction to stochastic processes. Linear processes. Autocovariance e autocorrelation.
  3. ARIMA modelling.
  4. The Box-Jenkins procedure.
  5. Introduction to forecasting methods.
  6. Decomposition and seasonal adjustments.
  7. Deterministic trends and stochastic trends. Unit root tests and complex dependence.

Modulo 2 - Analysis of financial time series

  1. Features of financial time series.
  2. Analysis of financial returns.
  3. Measuring volatility.
  4. Models for financial time series
    - volatility modelling;
    - ARCH/GARCH models;
  5. Forecasting with ARIMA models.

Readings/Bibliography

  1. T. Di Fonzo, F. Lisi, Serie storiche economiche, Carocci, Roma, 2015 (III Ristampa).
  2. G. M. Gallo, B. Pacini, Metodi quantitativi per i mercati finanziari, Carocci, Roma, 2013 (VII Ristampa).
  3. R.S. Tsay, Analysis of Financial Time Series, 3rd edition, Wiley, 2010

Teaching methods

  • Lectures.
  • Classes.
  • Lab sessions with case studies analysed with R.

Assessment methods

 

MODULE 1:

  • Lab exam with R

MODULE 2:

Written examination composed of

  • Theoretical questions
  • Exercises

It is possible to take separately the exam for each of the two Modules.

Teaching tools

  • Slides of the course
  • Exercises

Office hours

See the website of Simone Giannerini

See the website of Greta Goracci